What are Tokenizations and their Applications?

In AI, tokenizers are the tools that break down human text into smaller units called tokens. These tokens can be individual words, subwords, or even characters, depending on the setup of a specific tokenizer.

Tokenizers split your text into tokens and create a structured representation of your input data. This helps with things like analyzing your text, choosing the important details, and training machine learning models.

Tokenization extends its applications to various AI models. These include,

Deep Learning Models

Transformer models are the type of neural network that can process human language and create meaningful outcomes. They use tokenization to split the text into tokens. Tokens can vary in size (words, parts of words, individual letters). This depends on the specific model and the tasks it performs.

Embedding layers are vital parts of neural networks. They help translate human language into a specific code. For example, they translate “ball” into a specific code as “036”. From this, the computer can understand that “036” refers to the word “ball”.

BERT uses subword tokenization to manage unknown words. It uses the WordPiece tokenizer to break the text into subword tokens. It splits the word “Framework” into “Frame” and “## work”. The symbol “##” represents that “work” is the continuation of the previous subword “Frame”.

Multilingual AI

Tokenization enables multilingual AI models to understand various languages. It allows these models to handle various languages. Each language has its own set of vocabulary and grammar rules. This makes it difficult to break text into individual tokens.

Netflix analyzes user behavior to suggest personalized recommendations for you. If you watched a Korean movie last week, its algorithms can identify your pattern behaviors through tokenization. The movies on your suggestion can be tokenized on the basis of language, genre, and actors.

In multilingual social media platforms like Twitter, if you comment on a post “Arigato” in Japanese, a user from the United States can read your post. Tokenization can translate “Arigato” into “Thank You” in English. This helps people engage online regardless of their native languages.

Large Datasets

Tokenization for large datasets involves breaking down large-scale data for various purposes. It includes machine learning, data analysis, and data security. It is crucial in sectors like banking and e-commerce. Because it helps gain customers’ trust, and handling data easier. E-commerce platforms like Amazon employ tokenization.

While purchasing something, the credit card details like credit card number and available balance are put into tokenization. For example, if the credit card number is “1234 1234 1234,” it can turn into tokens as “AA11-BB11-CC11.” The next time a user is purchasing, there is no need to enter the card details.

WhatsApp uses tokenization to enhance data handling, privacy, and security. Tokenization allows WhatsApp to store vast amounts of data. If you want to search for a message, tokenization helps to find it easily. Your messages are tokenized in a way that only you and the recipient can read them.

Future of Tokenization in AI

Tokenization is getting better day by day. It is advancing in language support, textual understanding, customization, and ethical concerns. It opens up new opportunities in various fields like healthcare, supply chain management, finance, and investment.

In the future, assets like houses, stocks, and bonds might turn into digital tokens. It allows you to explore various investment opportunities in trend. You can even tokenize your brand logos and names to prove ownership. It prevents others from using similar logos and names without permission. This token system helps to stop violations and take legal action.

To wrap up

Tokenization paves the way for communication across boundaries. It doesn’t just stop there. Personalized movie suggestions and taking ownership of intellectual property are made possible. All other possibilities are limitless for AI with the help of tokenization. Stay updated for more updates on the advancements of tokenization in AI.